Deep-Elastic pp Scattering at LHC from Low-x Gluons

Deep-elastic pp scattering at c.m. energy 14 TeV at LHC in the momentum transfer range 4 GeV*2 < |t| < 10 GeV*2 is planned to be measured by the TOTEM group. We study this process in a model where the deep-elastic scattering is due to a single hard collision of a valence quark from one proton with a valence quark from the other proton. The hard collision originates from the low-x gluon cloud around one valence quark interacting with that of the other. The low-x gluon cloud can be identified as color glass condensate and has size ~0.3 F. Our prediction is that pp differential cross section in the large |t| region decreases smoothly as momentum transfer increases. This is in contrast to the prediction of pp differential cross section with visible oscillations and smaller cross sections by a large number of other models.Comment: 10 pages, including 4 figure

Local Properties of the Potential Energy Landscape of a Model Glass: Understanding the Low Temperature Anomalies

Though the existence of two-level systems (TLS) is widely accepted to explain low temperature anomalies in the sound absorption, heat capacity, thermal conductivity and other quantities, an exact description of their microscopic nature is still lacking. We performed computer simulations for a binary Lennard-Jones system, using a newly developed algorithm to locate double-well potentials (DWP) and thus two-level systems on a systematic basis. We show that the intrinsic limitations of computer simulations like finite time and finite size problems do not hamper this analysis. We discuss how the DWP are embedded in the total potential energy landscape. It turns out that most DWP are connected to the dynamics of the smaller particles and that these DWP are rather localized. However, DWP related to the larger particles are more collective

Backward correlations and dynamic heterogeneities: a computer study of ion dynamics

We analyse the correlated back and forth dynamics and dynamic heterogeneities, i.e. the presence of fast and slow ions, for a lithium metasilicate system via computer simulations. For this purpose we define, in analogy to previous work in the field of glass transition, appropriate three-time correlation functions. They contain information about the dynamics during two successive time intervals. First we apply them to simple model systems in order to clarify their information content. Afterwards we use this formalism to analyse the lithium trajectories. A strong back-dragging effect is observed, which also fulfills the time-temperature superposition principle. Furthermore, it turns out that the back-dragging effect is long-ranged and exceeds the nearest neighbor position. In contrast, the strength of the dynamic heterogeneities does not fulfill the time-temperature superposition principle. The lower the temperature, the stronger the mobility difference between fast and slow ions. The results are then compared with the simple model systems considered here as well as with some lattice models of ion dynamics.Comment: 12 pages, 10 figure

Self Consistent Screening Approximation For Critical Dynamics

We generalise Bray's self-consistent screening approximation to describe the critical dynamics of the $\phi^4$ theory. In order to obtain the dynamical exponent $z$, we have to make an ansatz for the form of the scaling functions, which fortunately can be much constrained by general arguments. Numerical values of $z$ for $d=3$, and $n=1,...,10$ are obtained using two different ans\"atze, and differ by a very small amount. In particular, the value of $z \simeq 2.115$ obtained for the 3-d Ising model agrees well with recent Monte-Carlo simulations.Comment: 21 pages, LaTeX file + 4 (EPS) figure

Autism with intellectual disability is associated with increased levels of maternal cytokines and chemokines during gestation.

Immune abnormalities have been described in some individuals with autism spectrum disorders (ASDs) as well as their family members. However, few studies have directly investigated the role of prenatal cytokine and chemokine profiles on neurodevelopmental outcomes in humans. In the current study, we characterized mid-gestational serum profiles of 22 cytokines and chemokines in mothers of children with ASD (N=415), developmental delay (DD) without ASD (N=188), and general population (GP) controls (N=428) using a bead-based multiplex technology. The ASD group was further divided into those with intellectual disabilities (developmental/cognitive and adaptive composite score&lt;70) (ASD+ID, N=184) and those without (composite score⩾70) (ASD-noID, N=201). Levels of cytokines and chemokines were compared between groups using multivariate logistic regression analyses, adjusting for maternal age, ethnicity, birth country and weight, as well as infant gender, birth year and birth month. Mothers of children with ASD+ID had significantly elevated mid-gestational levels of numerous cytokines and chemokines, such as granulocyte macrophage colony-stimulating factor, interferon-γ, interleukin-1α (IL-1α) and IL-6, compared with mothers of children with either ASD-noID, those with DD, or GP controls. Conversely, mothers of children with either ASD-noID or with DD had significantly lower levels of the chemokines IL-8 and monocyte chemotactic protein-1 compared with mothers of GP controls. This observed immunologic distinction between mothers of children with ASD+ID from mothers of children with ASD-noID or DD suggests that the intellectual disability associated with ASD might be etiologically distinct from DD without ASD. These findings contribute to the ongoing efforts toward identification of early biological markers specific to subphenotypes of ASD

Mode-coupling theory for multiple-time correlation functions of tagged particle densities and dynamical filters designed for glassy systems

The theoretical framework for higher-order correlation functions involving multiple times and multiple points in a classical, many-body system developed by Van Zon and Schofield [Phys. Rev. E 65, 011106 (2002)] is extended here to include tagged particle densities. Such densities have found an intriguing application as proposed measures of dynamical heterogeneities in structural glasses. The theoretical formalism is based upon projection operator techniques which are used to isolate the slow time evolution of dynamical variables by expanding the slowly-evolving component of arbitrary variables in an infinite basis composed of the products of slow variables of the system. The resulting formally exact mode-coupling expressions for multiple-point and multiple-time correlation functions are made tractable by applying the so-called N-ordering method. This theory is used to derive for moderate densities the leading mode coupling expressions for indicators of relaxation type and domain relaxation, which use dynamical filters that lead to multiple-time correlations of a tagged particle density. The mode coupling expressions for higher order correlation functions are also succesfully tested against simulations of a hard sphere fluid at relatively low density.Comment: 15 pages, 2 figure

Colloids in light fields: particle dynamics in random and periodic energy landscapes

The dynamics of colloidal particles in potential energy landscapes have mainly been investigated theoretically. In contrast, here we discuss the experimental realization of potential energy landscapes with the help of light fields and the observation of the particle dynamics by video microscopy. The experimentally observed dynamics in periodic and random potentials are compared to simulation and theoretical results in terms of, e.g. the mean-squared displacement, the time-dependent diffusion coefficient or the non-Gaussian parameter. The dynamics are initially diffusive followed by intermediate subdiffusive behaviour which again becomes diffusive at long times. How pronounced and extended the different regimes are, depends on the specific conditions, in particular the shape of the potential as well as its roughness or amplitude but also the particle concentration. Here we focus on dilute systems, but the dynamics of interacting systems in external potentials, and thus the interplay between particle-particle and particle-potential interactions, is also mentioned briefly. Furthermore, the observed dynamics of dilute systems resemble the dynamics of concentrated systems close to their glass transition, with which it is compared. The effect of certain potential energy landscapes on the dynamics of individual particles appears similar to the effect of interparticle interactions in the absence of an external potential

Complex lithium ion dynamics in simulated LiPO3 glass studied by means of multi-time correlation functions

Molecular dynamics simulations are performed to study the lithium jumps in LiPO3 glass. In particular, we calculate higher-order correlation functions that probe the positions of single lithium ions at several times. Three-time correlation functions show that the non-exponential relaxation of the lithium ions results from both correlated back-and-forth jumps and the existence of dynamical heterogeneities, i.e., the presence of a broad distribution of jump rates. A quantitative analysis yields that the contribution of the dynamical heterogeneities to the non-exponential depopulation of the lithium sites increases upon cooling. Further, correlated back-and-forth jumps between neighboring sites are observed for the fast ions of the distribution, but not for the slow ions and, hence, the back-jump probability depends on the dynamical state. Four-time correlation functions indicate that an exchange between fast and slow ions takes place on the timescale of the jumps themselves, i.e., the dynamical heterogeneities are short-lived. Hence, sites featuring fast and slow lithium dynamics, respectively, are intimately mixed. In addition, a backward correlation beyond the first neighbor shell for highly mobile ions and the presence of long-range dynamical heterogeneities suggest that fast ion migration occurs along preferential pathways in the glassy matrix. In the melt, we find no evidence for correlated back-and-forth motions and dynamical heterogeneities on the length scale of the next-neighbor distance.Comment: 12 pages, 13 figure

Microscopic estimation of the deformation potential in a structural model glass

Starting from a microscopic Hamiltonian we analyze the coupling between tunneling systems (TS&apos;s) and phonons in a structural model glass, chosen to describe NiP. We estimate the TS-phonon coupling constants, i.e. , the longitudinal and transverse deformation potentials yl and y, . They are proportional to the spatial distance between the two energy minima. This dependence translates into an energy dependence of the deformation potential, .which is relevant for the temperature dependence of the lowtemperature thermal conductivity. On the basis of TS s in NiP found via a computer search in our previous work we obtain the values y1=0.38 eV and yi/y, =2.35. We analyze the inAuence of the structure of typical TS&apos;s on the deformation potential. Together with the density of tunneling systems determined in a previous paper the interaction between tunneling systems can be calculated. In contrast to recent proposals, the weak-coupling picture and hence the validity of the standard tunneling model is confirmed

The Potential for Student Performance Prediction in Small Cohorts with Minimal Available Attributes

The measurement of student performance during their progress through university study provides academic leadership with critical information on each student’s likelihood of success. Academics have traditionally used their interactions with individual students through class activities and interim assessments to identify those “at risk” of failure/withdrawal. However, modern university environments, offering easy on-line availability of course material, may see reduced lecture/tutorial attendance, making such identification more challenging. Modern data mining and machine learning techniques provide increasingly accurate predictions of student examination assessment marks, although these approaches have focussed upon large student populations and wide ranges of data attributes per student. However, many university modules comprise relatively small student cohorts, with institutional protocols limiting the student attributes available for analysis. It appears that very little research attention has been devoted to this area of analysis and prediction. We describe an experiment conducted on a final-year university module student cohort of 23, where individual student data are limited to lecture/tutorial attendance, virtual learning environment accesses and intermediate assessments. We found potential for predicting individual student interim and final assessment marks in small student cohorts with very limited attributes and that these predictions could be useful to support module leaders in identifying students potentially “at risk.”.Peer reviewe